Time Series Classification
244 papers with code • 51 benchmarks • 17 datasets
Time Series Classification is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.
Source: Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
Libraries
Use these libraries to find Time Series Classification models and implementationsDatasets
Latest papers with no code
AdaFSNet: Time Series Classification Based on Convolutional Network with a Adaptive and Effective Kernel Size Configuration
Our model surpassed baseline models in terms of classification accuracy, underscoring the AdaFSNet network's efficiency and effectiveness in handling time series classification tasks.
Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models
In that regard, accelerometer data collected from neck collars can be used along with Machine Learning models to classify calf behaviour automatically.
Early detection of disease outbreaks and non-outbreaks using incidence data
In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur.
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification.
DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc.
InceptionTime vs. Wavelet -- A comparison for time series classification
Neural networks were used to classify infrasound data.
Multimodal Physical Fitness Monitoring (PFM) Framework Based on TimeMAE-PFM in Wearable Scenarios
Physical function monitoring (PFM) plays a crucial role in healthcare especially for the elderly.
LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification.
Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model
To address this challenge, we propose CrossTimeNet, a novel cross-domain SSL learning framework to learn transferable knowledge from various domains to largely benefit the target downstream task.